Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 2
Table of Contents
Оглавление1 Cover
5 Foreword
6 Preface
8 Part I INTELLIGENT COMPUTING AND APPLICATIONS 1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models 1.1 Introduction 1.2 Data Acquisition Method 1.3 Feature Extraction 1.4 Deep Learning Models 1.5 Results 1.6 Discussion 1.7 Advantages and Disadvantages of the Study 1.8 Limitations of the Study 1.9 Conclusion References 2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates 2.1 Introduction 2.2 Biological Neurons 2.3 Artificial Neural Networks 2.4 DNA Neural Networks 2.5 DNA Logic Gates 2.6 Advantages and Limitations 2.7 Conclusion Acknowledgment References 3 Intelligent Garment Detection Using Deep Learning 3.1 Introduction 3.2 Literature 3.3 Methodology 3.4 Experimental Results 3.5 Highlights 3.6 Conclusion and Future Works Acknowledgements References 4 Intelligent Computing on Complex Numbers for Cryptographic Applications 4.1 Introduction 4.2 Modular Arithmetic 4.3 Complex Plane 4.4 Matrix Algebra 4.5 Elliptic Curve Arithmetic 4.6 Cryptographic Applications 4.7 Conclusion References 5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies 5.1 Introduction 5.2 Machine/Deep Learning for Future Wireless Communication 5.3 Case Studies 5.4 Major Findings 5.5 Future Research Directions 5.6 Conclusion References 6 Designing of Routing Protocol for Crowd Associated Networks (CrANs) 6.1 Introduction 6.2 Background Study 6.3 CrANs 6.4 Simulation of MANET Network 6.5 Simulation of VANET Network 6.6 CrANs 6.7 Conclusion References 7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane 7.1 Introduction 7.2 Experimental Procedure 7.3 Methodology 7.4 Results and Discussions 7.5 Conclusions Acknowledgements References 8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach 8.1 Introduction 8.2 Literature Survey 8.3 Methodology 8.4 Dataset 8.5 Evaluation 8.6 Conclusion References 9 Image Classification by Reinforcement Learning With Two-State Q-Learning 9.1 Introduction 9.2 Proposed Approach 9.3 Datasets Used 9.4 Experimentation 9.5 Conclusion References 10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process 10.1 Introduction 10.2 Distributed Control System 10.3 Fuzzy Logic 10.4 Artificial Neural Network 10.5 Neuro-Fuzzy 10.6 Case Study 10.7 Software Implementation on Graphical User Interface 10.8 Results and Discussion 10.9 Discussion 10.10 Conclusion 10.11 Scope for Future Work References Appendix 10.1 MATLAB Simulation Configuration Using Sugeno Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model. Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model. 11 Artificial Neural Network in the Manufacturing Sectors 11.1 Introduction 11.2 Optimization 11.3 Artificial Neural Network: Optimization of Mechanical Systems 11.4 ANN vs. Human Brain 11.5 Architecture of Artificial Neural Networks 11.6 Learning Algorithm(s) 11.7 Different Type of Data 11.8 Case Study: Hard Machining of EN 31 Steel 11.9 Advantages of Using ANN in Manufacturing Sectors 11.10 Disadvantages of Using ANN in Manufacturing Sectors 11.11 Applications 11.12 Conclusions 11.13 Future Scope of ANN in Manufacturing Sectors References 12 Speech-Based Multilingual Translation Framework 12.1 Introduction 12.2 Literature Survey 12.3 Phases of ASR 12.4 Modules of ASR 12.5 Speech Database for ASR 12.6 Developing ASR 12.7 Performance of ASR 12.8 Application Areas 12.9 Conclusion and Future Work References 13 Text Summarization: A Technical Overview and Research Perspectives 13.1 Introduction 13.2 Summarization Techniques 13.3 Evaluating Summaries 13.4 Datasets and Results 13.5 Future Research Directions 13.6 Conclusion References 14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery 14.1 Introduction 14.2 Literature Review 14.3 Understanding the Google Cloud Platform 14.4 Using BigQuery in the Google Cloud Console 14.5 Sentiment Analysis 14.6 Turning to Google BigQuery Analysis 14.7 Proposed Method 14.8 Experimental Setup and Results 14.9 Conclusion References 15 A Review of Topic Modeling and Its Application 15.1 Introduction 15.2 Objective of Topic Modeling 15.3 Motivations and Contributions 15.4 Detailed Survey of Research Articles 15.5 Comparison Table of Previous Research 15.6 Expected Future Work 15.7 Conclusion References
9 Part II OPTIMIZATION 16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification 16.1 Introduction 16.2 Related Works 16.3 Methodology 16.4 Results and Discussion 16.5 Conclusion References 17 Optimal Inventory System in a Urea Bagging Industry 17.1 Introduction 17.2 Continuous Review Policy 17.3 Inventory Optimization Techniques 17.4 Model Formulation 17.5 Numerical Calculations 17.6 Conclusion References 18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario 18.1 Introduction 18.2 Mixed Integer Programming Methods 18.3 Introduction to Supply Chain Management System 18.4 Mathematical Model Formulation 18.5 Conclusion References 19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach 19.1 Introduction 19.2 Literature Review 19.3 The Aim and Objectives of the Study 19.4 Research Methodology 19.5 Study Results and Discussions 19.6 Conclusions References 20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode 20.1 Introduction 20.2 Designing of 90° SPS 20.3 Designing of Tunable Schiffman Phase Shifter 20.4 Major Finding and Limitation 20.5 Conclusion References 21 Optimizing Manufacturing Performance Through Fuzzy Techniques 21.1 Introduction 21.2 Literature Review 21.3 Performance Optimization through Fuzzy Techniques 21.4 Conclusions References 22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products 22.1 Introduction 22.2 Literature Review 22.3 Symbols and Assumptions 22.4 Model Formulation 22.5 Conclusion References
10 Part III META-HEURISTICS: APPLICATIONS AND INNOVATIONS 23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer 23.1 An Introduction to Optimization 23.2 Optimization and Engineering 23.3 Meta-Heuristic Optimization 23.4 Torquigener Albomaculosus 23.5 Pufferfish and Circular Structures 23.6 Results 23.7 Conclusion References 24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization 24.1 Introduction 24.2 Background on Sperm Swarm Optimization (SSO) and Grey Wolf Optimizer (GWO) 24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO) 24.4 Experiments and Results 24.5 Discussion 24.6 Conclusion References 25 State-of-the-Art Optimization and metaheuristic Algorithms 25.1 Introduction 25.2 An Overview of Traditional Optimization Approaches 25.3 Properties of Metaheuristics 25.4 Classification of Single Objective Metaheuristic Algorithms 25.5 Applications of Single Objective metaheuristic Approaches 25.6 Classification of Multi-Objective Optimization Algorithms 25.7 Hybridization of MOPs Algorithms 25.8 Parallel Multi-Objective Optimization 25.9 Applications of Multi-Objective Optimization 25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches 25.11 Conclusion 25.12 Major Findings, Future Scope of Metaheuristics and Its Applications 25.13 Limitations and Motivation of Metaheuristics Acknowledgements References 26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique 26.1 Introduction 26.2 Proposed Methodology 26.3 Simulation Results 26.4 Conclusions References 27 A New Parameter Estimation Technique of Three-Diode PV Cells 27.1 Introduction 27.2 Problem Statement 27.3 Proposed Method 27.4 Simulation Results and Discussions 27.5 Conclusions References
11 Part IV SUSTAINABLE COMPUTING 28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network 28.1 Introduction 28.2 System Model and Preliminaries 28.3 Machine Learning Techniques of Decision Fusion 28.4 Optimum Quantization of Decision Statistic and Fusion 28.5 Measurement Setup 28.6 Performance Evaluation 28.7 Conclusion 28.8 Limitations and Scope for Future Work References 29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies 29.1 Introduction 29.2 Green Approaches: Significance and Motivation 29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control 29.4 Green IoT–Based Smart Irrigation Monitoring 29.5 Technology Enablers for GIoT–Based Irrigation Monitoring 29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation 29.7 Other Recent Developments on GIoT–Based Smart Agriculture 29.8 Literature Review of Edge Computing–Based Irrigation Monitoring 29.9 LPWAN for GIoT–Based Smart Agriculture 29.10 Analysis and Discussion 29.11 Research Gap in GIoT–Based Precision Agriculture 29.12 Analysis of Merits and Shortcomings 29.13 Future Research Scope 29.14 Conclusion References 30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification 30.1 Introduction 30.2 Related Works 30.3 Proposed Approach 30.4 Experimental Details and Results 30.5 Conclusion References 31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission 31.1 Introduction 31.2 Related Work 31.3 Proposed Model 31.4 Complexity Analysis of Algorithms for Data Transmission 31.5 Experimental Analysis 31.6 Motivation and Limitations of Research 31.7 Conclusion 31.8 Future Work References 32 Intelligent Computing for Precision Agriculture 32.1 Introduction 32.2 Technology in Agriculture References 33 Intelligent Computing for Green Sustainability 33.1 Introduction 33.2 Modified DEMATEL 33.3 Weighted Sum Model 33.4 Weighted Product Model 33.5 Weighted Aggregated Sum Product Assessment 33.6 Grey Relational Analysis 33.7 Simple Multi-Attribute Rating Technique 33.8 Criteria Importance Through Inter-Criteria Correlation 33.9 Entropy 33.10 Evaluation Based on Distance From Average Solution 33.11 MOORA 33.12 Interpretive Structural Modeling 33.13 Conclusions 33.14 Limitations of the Study 33.15 Suggestions for Future Research References
12 Part V AI IN HEALTHCARE 34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease 34.1 Introduction 34.2 Methods 34.3 Statistical Analysis 34.4 Results 34.5 Discussion 34.6 Conclusion Acknowledgements References 35 Reconstruction of Dynamic MRI Using Convolutional LSTM Techniques 35.1 Introduction 35.2 Methodologies 35.3 Problem Formulation 35.4 Network Architecture 35.5 Results 35.6 Discussion 35.7 Conclusion References 36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion 36.1 Introduction 36.2 Literature Review 36.3 Multispectral Face Database 36.4 Methodology 36.5 Experiments 36.6 Results and Discussion 36.7 Conclusions Acknowledgments References 37 Polyp Detection Using Deep Neural Networks 37.1 Introduction 37.2 Literature Survey 37.3 Proposed Methodology 37.4 Implementation and Results 37.5 Conclusion and Future Work References 38 Boundary Exon Prediction in Human Sequences Using External Information Sources 38.1 Introduction 38.2 Proposed Exon Prediction Model 38.3 Homology-Based Exon Prediction 38.4 Results and Discussion 38.5 Conclusion 38.6 Motivation and Limitations of the Research 38.7 Major Findings of the Research References 39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform 39.1 Introduction 39.2 Sample Preparation 39.3 Methodology 39.4 Results and Discussion 39.5 Discussion 39.6 Conclusion 39.7 Future Scope Acknowledgement References 40 GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India—A Case of Gujarat 40.1 Introduction 40.2 The Rationale of the Study 40.3 Materials and Methodology 40.4 GIS and COVID-19 (Corona) Mapping 40.5 Results and Discussion 40.6 Conclusion References 41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi 41.1 Introduction 41.2 Hardware Design of Monitoring System 41.3 Software Design of Monitoring System 41.4 Working of ZigBee Module 41.5 Developed App for the Monitoring of Health 41.6 Google Fusion Table—Online Database 41.7 Application Developed for Health Monitoring System 41.8 Conclusion and Future Work References
13 Index
